% Bahn analysis report % author: “Jinshi” % date: “March 26, 2019”

output:
html_document:
df_print: paged
bibliography: bibliography.ris

1. The spatial destribution of global Rs sites

Global spatial destribution of soil repiration sites

Global spatial destribution of soil repiration sites

It is obvious that Rs measurements from cold region is more importent, but how? Improve the Rs measure equipment so it can measure Rs in cold condition; Increasing funds; * Measured once per day to get daily mean

Rs measured at diurnal soil temperature

Rs measured at diurnal soil temperature

Rs measured at mean annual soil temperature

Rs measured at mean annual soil temperature

2. The object of this analysis are

3. Mthods

3.1 Data used from SRDB_V4

  • Climate space figure
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3.2 Ts sources (MGRsD, MGRsD_TAIR, From paper, Rs_Ts_relationship)

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3.3 Annual Rs or Ts coverage effect

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3.4.1 Effect of maximum allowed divergence between global climate data set and site-specific air temperature

  • Does TAIR_dev and TAIR_LT<_dev affect the relationship – YES!!!!!
  • Does TAIR_LTM_dev () pull the slope off 1? – YES!!!!!
  • TAIR_LTM_dev = with( srdb, abs( MAT_Del - MAT ) )
  • TAIR_dev <- with( srdb, abs( TAnnual_Del - Study_temp ) )
  • Figure E. Effect of maximum allowed divergence between global climate data set and site-specific air temperature, when given. As we throw out data points with high divergence, R2 goes up (top panel) and RSE goes down (bottom, g C m-2 yr-1).
  • This is different from what SPI test
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3.4.2 Effect of maximum allowed divergence between annual precipitation from paper and Del

3.5 test the relationship between Rs_annual and Rs_mat

## Fri Mar 29 18:02:52 2019  -------------------+++++-------------------
## Fri Mar 29 18:02:52 2019  Bahn relationship for these data:
## 
## Call:
## lm(formula = Rs_annual ~ Rs_TAIR, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -746.75 -111.49  -39.62   86.43 1273.96 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 160.04546   13.25471   12.07   <2e-16 ***
## Rs_TAIR       0.94510    0.01624   58.21   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 194.7 on 821 degrees of freedom
## Multiple R-squared:  0.8049, Adjusted R-squared:  0.8047 
## F-statistic:  3388 on 1 and 821 DF,  p-value: < 2.2e-16

4. Results

4.1 Using Ts, TAnnual or MAT

4.1.1 Using soil temperature

## 
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1269.9  -106.0    18.3   117.3  1222.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.58745   17.22279  -0.266     0.79    
## Rs_annual    1.07455    0.01846  58.222   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 233.1 on 821 degrees of freedom
## Multiple R-squared:  0.805,  Adjusted R-squared:  0.8048 
## F-statistic:  3390 on 1 and 821 DF,  p-value: < 2.2e-16

## Fri Mar 29 18:02:53 2019  -------------------+++++-------------------
## Fri Mar 29 18:02:53 2019  How are Rs_annual and Rs_annual_bahn_Temp related?
## Fri Mar 29 18:02:53 2019  sdata rows = 823 cols = 142
## Fri Mar 29 18:02:53 2019  Model summary:
## 
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1269.9  -106.0    18.3   117.3  1222.3 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -4.58745   17.22279  -0.266     0.79    
## Rs_annual    1.07455    0.01846  58.222   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 233.1 on 821 degrees of freedom
## Multiple R-squared:  0.805,  Adjusted R-squared:  0.8048 
## F-statistic:  3390 on 1 and 821 DF,  p-value: < 2.2e-16
## 
## Fri Mar 29 18:02:53 2019  Plotting and saving model diagnostics...
## Fri Mar 29 18:02:53 2019  Plotting and saving model residuals...
## Fri Mar 29 18:02:53 2019  Saving outputs/3-modelresids.pdf
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## Fri Mar 29 18:02:53 2019  Test H0 of intercept=0: p-value = 0.7900293
## Fri Mar 29 18:02:53 2019  Test H0 of slope=1: p-value = 5.863901e-05
## [1] 0.7900293
## [1] 5.863901e-05

4.1.2 Using T_Annual

## 
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2450.0  -173.0    -0.3   151.8  4964.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -29.30007   27.53434  -1.064    0.288    
## Rs_annual     1.03037    0.02951  34.921   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 372.7 on 821 degrees of freedom
## Multiple R-squared:  0.5976, Adjusted R-squared:  0.5971 
## F-statistic:  1219 on 1 and 821 DF,  p-value: < 2.2e-16

## Fri Mar 29 18:02:54 2019  -------------------+++++-------------------
## Fri Mar 29 18:02:54 2019  How are Rs_annual and Rs_annual_bahn_Temp related?
## Fri Mar 29 18:02:54 2019  sdata rows = 823 cols = 142
## Fri Mar 29 18:02:54 2019  Model summary:
## 
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2450.0  -173.0    -0.3   151.8  4964.6 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -29.30007   27.53434  -1.064    0.288    
## Rs_annual     1.03037    0.02951  34.921   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 372.7 on 821 degrees of freedom
## Multiple R-squared:  0.5976, Adjusted R-squared:  0.5971 
## F-statistic:  1219 on 1 and 821 DF,  p-value: < 2.2e-16
## 
## Fri Mar 29 18:02:54 2019  Plotting and saving model diagnostics...
## Fri Mar 29 18:02:54 2019  Plotting and saving model residuals...
## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## Fri Mar 29 18:02:54 2019  Saving outputs/3-modelresids.pdf
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## Warning: Removed 2 rows containing non-finite values (stat_smooth).

## Warning: Removed 2 rows containing missing values (geom_point).

## Fri Mar 29 18:02:54 2019  Test H0 of intercept=0: p-value = 0.2875834
## Fri Mar 29 18:02:54 2019  Test H0 of slope=1: p-value = 0.3037032
## [1] 0.2875834
## [1] 0.3037032

4.1.3 Using MAT

## 
## Call:
## lm(formula = bahn ~ Rs_annual, data = sdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2356.27  -167.25    -1.34   142.82  2746.39 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -32.36603   24.27741  -1.333    0.183    
## Rs_annual     1.00324    0.02602  38.563   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 328.6 on 821 degrees of freedom
## Multiple R-squared:  0.6443, Adjusted R-squared:  0.6439 
## F-statistic:  1487 on 1 and 821 DF,  p-value: < 2.2e-16

## Fri Mar 29 18:02:55 2019  -------------------+++++-------------------
## Fri Mar 29 18:02:55 2019  How are Rs_annual and Rs_annual_bahn_Temp related?
## Fri Mar 29 18:02:55 2019  sdata rows = 823 cols = 142
## Fri Mar 29 18:02:55 2019  Model summary:
## 
## Call:
## lm(formula = temp ~ Rs_annual, data = sdata)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2356.27  -167.25    -1.34   142.82  2746.39 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -32.36603   24.27741  -1.333    0.183    
## Rs_annual     1.00324    0.02602  38.563   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 328.6 on 821 degrees of freedom
## Multiple R-squared:  0.6443, Adjusted R-squared:  0.6439 
## F-statistic:  1487 on 1 and 821 DF,  p-value: < 2.2e-16
## 
## Fri Mar 29 18:02:55 2019  Plotting and saving model diagnostics...
## Fri Mar 29 18:02:55 2019  Plotting and saving model residuals...
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## Fri Mar 29 18:02:55 2019  Saving outputs/3-modelresids.pdf
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## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

## Fri Mar 29 18:02:55 2019  Test H0 of intercept=0: p-value = 0.1828444
## Fri Mar 29 18:02:55 2019  Test H0 of slope=1: p-value = 0.9008364
## [1] 0.1828444
## [1] 0.9008364

4.2 Analysis when Rs_mat cannot represent Rs_annual

4.2.1 Test extreme high Rs values (>3000)

  • unlikely these extremes high Rs values have big influence
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4.2.2 Does Ecosystem_type affects the relationship between Rs_annual and Rs_mat

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4.2.3 Does Meas_method affects the relationship

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4.2.4 RA or RH dominated sites differ?

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4.2.5 Biome effect

4.2.6 TAIR and precipitation variability affect?

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4.2.7 Does drought affect?

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## Warning in qt((1 - level)/2, df): NaNs produced

5. Monthly and daily results

6. More analysis in the future